Fetching the paper…
Reading the bibliography…
Real-world applications of machine learning tools in high-stakes domains are often regulated to be fair, in the sense that the predicted target should satisfy some quantitative notion of parity with respect to a protected attribute.
Adversarial privacy preservation under attribute inference attack
Han Zhao, Jianfeng Chi, Yuan Tian, and Geoffrey J. Gordon · 1906
Earlier work this paper cites.
Conditional learning of fair representations
Han Zhao, Amanda Coston, Tameem Adel, and Geoffrey J. Gordon · 1910
Earlier work this paper cites.
Eine informationstheoretische ungleichung und ihre anwendung auf beweis der ergodizitaet von markoffschen ketten
Imre Csiszár · 1964
Earlier work this paper cites.
A general class of coefficients of divergence of one distribution from another
Syed Mumtaz Ali and Samuel D Silvey · 1966
Earlier work this paper cites.
Information-type measures of difference of probability distributions and indirect observation
Imre Csiszár · 1967
Earlier work this paper cites.
Divergence measures based on the shannon entropy
Jianhua Lin · 1991
Earlier work this paper cites.
Game theory, maximum entropy, minimum discrepancy and robust bayesian decision theory
Peter D Grünwald, A Philip Dawid, et al · 2004
Earlier work this paper cites.
On divergences and informations in statistics and information theory
Friedrich Liese and Igor Vajda · 2006
Earlier work this paper cites.
Confliction of the convexity and metric properties in f-divergences
Mohammadali Khosravifard, Dariush Fooladivanda, and T Aaron Gulliver · 2007
Earlier work this paper cites.
Building classifiers with independency constraints
Toon Calders, Faisal Kamiran, and Mykola Pechenizkiy · 2009
Earlier work this paper cites.
Classifying without discriminating
Faisal Kamiran and Toon Calders · 2009
Earlier work this paper cites.
Fairness-aware learning through regularization approach
Toshihiro Kamishima, Shotaro Akaho, and Jun Sakuma · 2011
Earlier work this paper cites.
Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
Earlier work this paper cites.
Fairness-aware classifier with prejudice remover regularizer
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma · 2012
Earlier work this paper cites.
Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
Earlier work this paper cites.
Censoring representations with an adversary
Harrison Edwards and Amos Storkey · 2015
Earlier work this paper cites.
The variational fair autoencoder
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard Zemel · 2015
Earlier work this paper cites.
Fairness constraints: Mechanisms for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi · 2015
Cited alongside, same era.
On the relation between accuracy and fairness in binary classification
Indre Zliobaite · 2015
Cited alongside, same era.
Big data’s disparate impact
Solon Barocas and Andrew D Selbst · 2016
Cited alongside, same era.
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
Cited alongside, same era.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, Nati Srebro, et al · 2016
Cited alongside, same era.
Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
Later among the works it cites.
Learning adversarially fair and transferable representations
David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel · 2018
Later among the works it cites.
The cost of fairness in binary classification
Aditya Krishna Menon and Robert C Williamson · 2018
Later among the works it cites.
Translation tutorial: 21 fairness definitions and their politics
Arvind Narayanan · 2018
Later among the works it cites.
Mitigating unwanted biases with adversarial learning
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell · 2018
Later among the works it cites.
One-network adversarial fairness
Tameem Adel, Isabel Valera, Zoubin Ghahramani, and Adrian Weller · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2016
Cited alongside, same era.
A statistical framework for fair predictive algorithms
Kristian Lum and James Johndrow · 2016
Cited alongside, same era.
Fairness in machine learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2017
Cited alongside, same era.
Data decisions and theoretical implications when adversarially learning fair representations
Alex Beutel, Jilin Chen, Zhe Zhao, and Ed H Chi · 2017
Cited alongside, same era.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2017
Cited alongside, same era.
Algorithmic decision making and the cost of fairness
Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq · 2017
Cited alongside, same era.
Minimax filter: Learning to preserve privacy from inference attacks
Jihun Hamm · 2017
Cited alongside, same era.
An algorithm for removing sensitive information: application to race-independent recidivism prediction
James E Johndrow, Kristian Lum, et al · 2019
Closest in time.
Learning controllable fair representations
Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, and Stefano Ermon · 2019
Closest in time.
Inherent tradeoffs in learning fair representations
Han Zhao and Geoff Gordon · 2019
Closest in time.
Recovering from biased data: Can fairness constraints improve accuracy?
Avrim Blum and Kevin Stangl · 2020
Closest in time.
Fair regression with wasserstein barycenters
Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, and Massimiliano Pontil · 2020
Closest in time.
Is there a trade-off between fairness and accuracy? a perspective using mismatched hypothesis testing
Sanghamitra Dutta, Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu, and Kush Varshney · 2020
Closest in time.
Projection to fairness in statistical learning
Thibaut Le Gouic, Jean-Michel Loubes, and Philippe Rigollet · 2020
Closest in time.
On learning language-invariant representations for universal machine translation
Han Zhao, Junjie Hu, and Andrej Risteski · 2020
Closest in time.
Understanding and mitigating accuracy disparity in regression
Jianfeng Chi, Yuan Tian, Geoffrey J Gordon, and Han Zhao · 2021
Closest in time.
General data protection regulation
GDPR · 2021
Closest in time.
Costs and benefits of wasserstein fair regression
Han Zhao · 2021
Closest in time.